Air traffic complexity is usually defined as difficulty of monitoring and managing a specific air traffic situation. Since it is a psychological construct, best measure of complexity is that given by air traffic controllers. However, there is a need to make a method for complexity estimation which can be used without constant controller input. So far, mostly linear models were used. Here, the possibility of using artificial neural networks for complexity estimation is explored. Genetic algorithm has been used to search for the best artificial neural network configuration. The conclusion is that the artificial neural networks perform as well as linear models and that the remaining error in complexity estimation can only be explained as inter-rater or intra-rater unreliability. One advantage of artificial neural networks in comparison to linear models is that the data do not have to be filtered based on the concept of operations (conventional vs. trajectory-based).


    Access

    Download


    Export, share and cite



    Title :

    Subjective Air Traffic Complexity Estimation Using Artificial Neural Networks


    Contributors:


    Publication date :

    2019




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




    Road Traffic Prediction Using Artificial Neural Networks

    Loumiotis, Ioannis / Demestichas, Konstantinos / Adamopoulou, Evgenia et al. | IEEE | 2018


    Traffic Mark Classification Using Artificial Neural Networks

    Hua, J. / Faghri, A. / ASCE | British Library Conference Proceedings | 1993


    Traffic complexity estimation

    YANG HSIN-HSIANG / PRAKAH-ASANTE KWAKU O / TSENG FLING FINN et al. | European Patent Office | 2017

    Free access

    TRAFFIC COMPLEXITY ESTIMATION

    YANG HSIN-HSIANG / PRAKAH-ASANTE KWAKU O / TSENG FLING FINN et al. | European Patent Office | 2016

    Free access

    Traffic complexity estimation

    HSIN-HSIANG YANG / KWAKU O PRAKAH-ASANTE / FINN TSENG et al. | European Patent Office | 2016

    Free access